Diaspora communities are disproportionately impacted by off-the-radar
misinformation and often neglected by mainstream fact-checking efforts,
creating a critical need to scale-up efforts of nascent fact-checking
initiatives. In this paper we present SynDy, a framework for Synthetic Dynamic
Dataset Generation to leverage the capabilities of the largest frontier Large
Language Models (LLMs) to train local, specialized language models. To the best
of our knowledge, SynDy is the first paper utilizing LLMs to create
fine-grained synthetic labels for tasks of direct relevance to misinformation
mitigation, namely Claim Matching, Topical Clustering, and Claim Relationship
Classification. SynDy utilizes LLMs and social media queries to automatically
generate distantly-supervised, topically-focused datasets with synthetic labels
on these three tasks, providing essential tools to scale up human-led
fact-checking at a fraction of the cost of human-annotated data. Training on
SynDy's generated labels shows improvement over a standard baseline and is not
significantly worse compared to training on human labels (which may be
infeasible to acquire). SynDy is being integrated into Meedan's chatbot
tiplines that are used by over 50 organizations, serve over 230K users
annually, and automatically distribute human-written fact-checks via messaging
apps such as WhatsApp. SynDy will also be integrated into our deployed
Co-Insights toolkit, enabling low-resource organizations to launch tiplines for
their communities. Finally, we envision SynDy enabling additional fact-checking
tools such as matching new misinformation claims to high-quality explainers on
common misinformation topics.

SynDy 是一种利用最先进的大型语言模型，通过合成动态数据集的方法，为舆情封控提供了重要工具，用于针对声称匹配、专题聚类和声称关系分类等任务生成细粒度的合成标签，以便进行大规模人工事实核查。

SynDy: 用于虚假信息任务的合成动态数据集生成框架

SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation  Tasks

Large Language Models have emerged as prime candidates to tackle
misinformation mitigation. However, existing approaches struggle with
hallucinations and overconfident predictions. We propose an uncertainty
quantification framework that leverages both direct confidence elicitation and
sampled-based consistency methods to provide better calibration for NLP
misinformation mitigation solutions. We first investigate the calibration of
sample-based consistency methods that exploit distinct features of consistency
across sample sizes and stochastic levels. Next, we evaluate the performance
and distributional shift of a robust numeric verbalization prompt across single
vs. two-step confidence elicitation procedure. We also compare the performance
of the same prompt with different versions of GPT and different numerical
scales. Finally, we combine the sample-based consistency and verbalized methods
to propose a hybrid framework that yields a better uncertainty estimation for
GPT models. Overall, our work proposes novel uncertainty quantification methods
that will improve the reliability of Large Language Models in misinformation
mitigation applications.

我们提出一种不确定性量化框架，利用直接置信度询问和基于样本的一致性方法，为 NLP 的假新闻抵制解决方案提供更好的校准，通过研究基于样本的一致性方法的校准，评估坚固的数值化提示的性能和分布偏移，结合基于样本的一致性和指示性方法提出一种混合框架，提高大型语言模型在假新闻抵制应用中的可靠性。

结合置信度引导和基于样本的方法进行虚假信息缓解中的不确定性量化

Combining Confidence Elicitation and Sample-based Methods for  Uncertainty Quantification in Misinformation Mitigation

Recent social networks' misinformation mitigation approaches tend to
investigate how to reduce misinformation by considering a whole-network
statistical scale. However, unbalanced misinformation exposures among
individuals urge to study fair allocation of mitigation resources. Moreover,
the network has random dynamics which change over time. Therefore, we introduce
a stochastic and non-stationary knapsack problem, and we apply its resolution
to mitigate misinformation in social network campaigns. We further propose a
generic misinformation mitigation algorithm that is robust to different social
networks' misinformation statistics, allowing a promising impact in real-world
scenarios. A novel loss function ensures fair mitigation among users. We
achieve fairness by intelligently allocating a mitigation incentivization
budget to the knapsack, and optimizing the loss function. To this end, a team
of Learning Automata (LA) drives the budget allocation. Each LA is associated
with a user and learns to minimize its exposure to misinformation by performing
a non-stationary and stochastic walk over its state space. Our results show how
our LA-based method is robust and outperforms similar misinformation mitigation
methods in how the mitigation is fairly influencing the network users.

介绍了一种应对社交网络中误导信息的解决方案，利用随机和非恒定多重背包问题得出一种新的算法，使用学习自动机来实现公平分配，以实现对社交网络用户的公正影响。